UMCU / Cardio_MacroCVD_CTReport_Dutch_DeBERTa

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Total runs: 8
24-hour runs: 0
7-day runs: 1
30-day runs: 3
Model's Last Updated: June 18 2026
text-classification

Introduction of Cardio_MacroCVD_CTReport_Dutch_DeBERTa

Model Details of Cardio_MacroCVD_CTReport_Dutch_DeBERTa

We used GPT4.1 to extract a binary macro-vascular-disease label from more than 7000 Dutch CT Heart reports in the University Medical Center Utrecht.

In parallel we developed a heuristics pipeline based on CADRADS>2, MESA>65%, CACS>400 and a stenosis/occlusion>=50%, together with regular expressions regarding the macro-vascular disease level.

This model is trained on the labels extracted with GPT-4.1, these labels were consistent with the heuristics-based labeling pipeline, with a correlation score of ahout about 0.83(Pearson).

In 10-fold cross-validation the model scored f1,precision and recall of about 93%, the model uploaded here was 1 fold from a 40-fold split, and obtained 93% (f1,prec,rec). Note: this scoring was obtained with the default 0.5 proba threshold.

Runs of UMCU Cardio_MacroCVD_CTReport_Dutch_DeBERTa on huggingface.co

8
Total runs
0
24-hour runs
-3
3-day runs
1
7-day runs
3
30-day runs

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Cardio_MacroCVD_CTReport_Dutch_DeBERTa is an open source model from GitHub that offers a free installation service, and any user can find Cardio_MacroCVD_CTReport_Dutch_DeBERTa on GitHub to install. At the same time, huggingface.co provides the effect of Cardio_MacroCVD_CTReport_Dutch_DeBERTa install, users can directly use Cardio_MacroCVD_CTReport_Dutch_DeBERTa installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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Total runs: 4
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Growth Rate: 0.00%
Updated:October 03 2025